Predição Just-In-Time de Defeitos em Software Utilizando Inteligência Artificial
Resumo
Durante o desenvolvimento ou modificação de um software, deve ser garantido que o produto final chegue ao usuário com a menor quantidade de erros possíveis. Métodos de predição de defeitos em software podem ser usados para isso. Neste artigo é apresentado um estudo utilizando, para a solução do problema de identificação de erros Just-In-Time (JIT), rede neural artificial (Artificial Neural Network - ANN) e árvore de decisão (Decision Tree - DT). As bases de dados utilizadas como treino, teste e validação neste trabalho foram as mesmas utilizadas e compiladas por [Kamei et al. 2013]. Os resultados obtidos, tanto com a ANN e com a DT são em média superiores aos trabalhos de [Kamei et al. 2013] e [Yang et al. 2017].
Referências
Arar, O. F., and Ayan, K. (2015) “Software Defect Prediction Using Costsensitive Neural Network”. Applied Soft Computing, v. 33, p. 263-277.
Batista, B. C. F.; (2012) “Soluções de Equações Diferenciais Usando Redes Neurais de Múltiplas Camadas Com os Métodos da Descida Mais Íngreme e Levenberg- Marquardt”. (Doctoral dissertation, Dissertação de mestrado, PPGME-ICEN-UFPA).
Breiman, L.; (1996) “Some Properties of Splitting Criteria”. Machine Learning, 24(1), 41-47.
Fenton, N. E. and Neil, M.; (1999) “A Critique of Software Defect Prediction Models”. IEEE Transactions on software engineering, v.25, n. 5, p. 675-689.
Figueiredo Filho, D. B., & Silva Júnior, J. A. D. (2009). Desvendando os Mistérios do Coeficiente de Correlação de Pearson (r).
Gayathri, M., and Sudha, A.; (2014) “Software Defect Prediction System Using Multilayer Perceptron Neural Network With Data Mining”. International Journal of Recent Tecnology and Engineering (IJRTE). v. 3, n. 2, p. 54-59.
Haykin, S.; (2001) “Redes Neurais: Princípios e Prática”. Bookman Editora.
Okutan, Ahmet; Yildz, Olcay Taner. (2014) “Software Defect Prediction Using Bayesian Networks”, Empirical Software Engineering, v. 19, n. 1, p. 154-181.
Jindal, R., Malhotra, R., and Jain, A. (2014). “Software defect prediction using neural networks”. In: Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 3rd International Conference on, pages 16. IEEE.
Kamei, Y., Shihab, E., Adams, B., Hassan, A. E., Mockus, A., Sinha, A., And Ubayashi, N.; (2013) “A Large-Scale Empirical Study of Just-In-Time Quality Assurance”. IEEE Transactions on Software Engineering, v. 39, n. 6, p. 757-773.
Kamei, Y.; JIT Databases. Disponível em: http://research.cs.queensu.ca/~kamei/jittse/jit.zip. Acesso em: 18 mar. 2019 .
Li, J., He, P., Zhu, J., And Lyu, M. R.; (2017) “Software Defect Prediction Via Convolutional Neural Network”. In Software Quality, Reliability and Security (QRS), 2017 IEEE International Conference on, pages 318328. IEEE.
Pearson, Karl; Fisher, Ronald & Inman, Henry F. (1994), “Karl Pearson and R. A. Fisher on Statistical Tests: A 1935 Exchange from Nature”. The American Statistician, 48,1: 2-11.
Quinlan, J. R.; (2014) “C4.5: Programs For Machine Learning”. Elsevier.
Rokach, L., and Maimon, O. Z.; (2008) “Data Mining With Decision Trees: Theory and Applications” (Vol. 69). World scientific.
Scikit-Learn. Machine Learning in Python. Disponível em: http://scikit-learn.org/stable/index.html. Acesso em: 18 mar. 2019a.
Scikit-Learn. Machine Learning in Python. Disponível em: https://scikit- learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html. Acesso em: 18 mar. 2019b.
Theodoridis, S.; Koutroumbas, K.; (2009) “Pattern Recognition”. 3. ed. San Diego: Academic Press.
Yang, X.; (2015) “Deep Learning for Just-In-Time Defect Prediction”. In QRS. p.17-26.
Yang, X., LO, D., XIA, X., and SUN, J.; (2017) “Tlel: A Two-Layer Ensemble Learning Approach for Just-In-Time Defect Prediction”. Information and Software Technology, v.87, p. 206-220.
Yu, Hao, and Wilamowski, B. M.; (2011) “Levenberg–Marquardt Training” Industrial Electronics Handbook, vol. 5 – Intelligent Systems, 2nd Edition, chapter 12, pp. 12-1 to 12-15, CRC Press.
Batista, B. C. F.; (2012) “Soluções de Equações Diferenciais Usando Redes Neurais de Múltiplas Camadas Com os Métodos da Descida Mais Íngreme e Levenberg- Marquardt”. (Doctoral dissertation, Dissertação de mestrado, PPGME-ICEN-UFPA).
Breiman, L.; (1996) “Some Properties of Splitting Criteria”. Machine Learning, 24(1), 41-47.
Fenton, N. E. and Neil, M.; (1999) “A Critique of Software Defect Prediction Models”. IEEE Transactions on software engineering, v.25, n. 5, p. 675-689.
Figueiredo Filho, D. B., & Silva Júnior, J. A. D. (2009). Desvendando os Mistérios do Coeficiente de Correlação de Pearson (r).
Gayathri, M., and Sudha, A.; (2014) “Software Defect Prediction System Using Multilayer Perceptron Neural Network With Data Mining”. International Journal of Recent Tecnology and Engineering (IJRTE). v. 3, n. 2, p. 54-59.
Haykin, S.; (2001) “Redes Neurais: Princípios e Prática”. Bookman Editora.
Okutan, Ahmet; Yildz, Olcay Taner. (2014) “Software Defect Prediction Using Bayesian Networks”, Empirical Software Engineering, v. 19, n. 1, p. 154-181.
Jindal, R., Malhotra, R., and Jain, A. (2014). “Software defect prediction using neural networks”. In: Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 3rd International Conference on, pages 16. IEEE.
Kamei, Y., Shihab, E., Adams, B., Hassan, A. E., Mockus, A., Sinha, A., And Ubayashi, N.; (2013) “A Large-Scale Empirical Study of Just-In-Time Quality Assurance”. IEEE Transactions on Software Engineering, v. 39, n. 6, p. 757-773.
Kamei, Y.; JIT Databases. Disponível em: http://research.cs.queensu.ca/~kamei/jittse/jit.zip. Acesso em: 18 mar. 2019 .
Li, J., He, P., Zhu, J., And Lyu, M. R.; (2017) “Software Defect Prediction Via Convolutional Neural Network”. In Software Quality, Reliability and Security (QRS), 2017 IEEE International Conference on, pages 318328. IEEE.
Pearson, Karl; Fisher, Ronald & Inman, Henry F. (1994), “Karl Pearson and R. A. Fisher on Statistical Tests: A 1935 Exchange from Nature”. The American Statistician, 48,1: 2-11.
Quinlan, J. R.; (2014) “C4.5: Programs For Machine Learning”. Elsevier.
Rokach, L., and Maimon, O. Z.; (2008) “Data Mining With Decision Trees: Theory and Applications” (Vol. 69). World scientific.
Scikit-Learn. Machine Learning in Python. Disponível em: http://scikit-learn.org/stable/index.html. Acesso em: 18 mar. 2019a.
Scikit-Learn. Machine Learning in Python. Disponível em: https://scikit- learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html. Acesso em: 18 mar. 2019b.
Theodoridis, S.; Koutroumbas, K.; (2009) “Pattern Recognition”. 3. ed. San Diego: Academic Press.
Yang, X.; (2015) “Deep Learning for Just-In-Time Defect Prediction”. In QRS. p.17-26.
Yang, X., LO, D., XIA, X., and SUN, J.; (2017) “Tlel: A Two-Layer Ensemble Learning Approach for Just-In-Time Defect Prediction”. Information and Software Technology, v.87, p. 206-220.
Yu, Hao, and Wilamowski, B. M.; (2011) “Levenberg–Marquardt Training” Industrial Electronics Handbook, vol. 5 – Intelligent Systems, 2nd Edition, chapter 12, pp. 12-1 to 12-15, CRC Press.
Publicado
09/07/2019
Como Citar
RAMOS, Ismael Araújo; AMORA, Márcio André Baima.
Predição Just-In-Time de Defeitos em Software Utilizando Inteligência Artificial. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 46. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 113-124.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2019.6572.
